As artificial intelligence AI becomes increasingly embedded in condition monitoring systems across industrial and energy environments, the belief that algorithms can fully replace human diagnostic expertise is gaining traction. But this assumption is deeply flawed, says Annemie Willer, manager of the Asset Reliability Care division at WearCheck, who warns that machine health assessment still depends on human context, experience and engineering insight.
Willer explains that claims about AIs ability to fuse vibration, oil analysis, thermography, process data, ultrasound and acoustic emission into a single perfect picture of machine health are misplaced. Throwing more data at an algorithm does not automatically produce a complete or accurate diagnosis. It may sound compelling, she notes, but it ignores how machines behave in the real world.
Machines are not clones. Even identical pumps from the same OEM, installed in the same plant under the same conditions, will not age in the same way. One might run smoothly for years while another fails prematurely and no amount of sensor data can reliably explain that difference. Assets accumulate unique wear histories, respond differently to stresses and operator behaviour and are shaped by human intervention, process fluctuations and subtle variances in assembly. AI cannot capture those realities.